no code implementations • 18 Feb 2024 • Alberto Abadie, Anish Agarwal, Raaz Dwivedi, Abhin Shah
This article introduces a new estimator of average treatment effects under unobserved confounding in modern data-rich environments featuring large numbers of units and outcomes.
no code implementations • 26 Dec 2023 • Daniel Ngo, Keegan Harris, Anish Agarwal, Vasilis Syrgkanis, Zhiwei Steven Wu
We consider the setting of synthetic control methods (SCMs), a canonical approach used to estimate the treatment effect on the treated in a panel data setting.
no code implementations • 21 Jun 2023 • Alberto Abadie, Anish Agarwal, Guido Imbens, Siwei Jia, James McQueen, Serguei Stepaniants
Business/policy decisions are often based on evidence from randomized experiments and observational studies.
1 code implementation • NeurIPS 2023 • Abhineet Agarwal, Anish Agarwal, Suhas Vijaykumar
Our goal is to learn unit-specific potential outcomes for any combination of these $p$ interventions, i. e., $N \times 2^p$ causal parameters.
no code implementations • 25 Nov 2022 • Keegan Harris, Anish Agarwal, Chara Podimata, Zhiwei Steven Wu
Unlike this classical setting, we permit the units generating the panel data to be strategic, i. e. units may modify their pre-intervention outcomes in order to receive a more desirable intervention.
no code implementations • 20 Oct 2022 • Anish Agarwal, Vasilis Syrgkanis
Our work avoids the combinatorial explosion in the number of units that would be required by a vanilla application of prior synthetic control and synthetic intervention methods in such dynamic treatment regime settings.
no code implementations • 20 Oct 2022 • Anish Agarwal, Sarah H. Cen, Devavrat Shah, Christina Lee Yu
We propose an estimator, Network Synthetic Interventions (NSI), and show that it consistently estimates the mean outcomes for a unit under an arbitrary set of counterfactual treatments for the network.
1 code implementation • 5 Jan 2022 • Abdullah Alomar, Pouya Hamadanian, Arash Nasr-Esfahany, Anish Agarwal, Mohammad Alizadeh, Devavrat Shah
Key to CausalSim is mapping unbiased trace-driven simulation to a tensor completion problem with extremely sparse observations.
no code implementations • 30 Sep 2021 • Anish Agarwal, Munther Dahleh, Devavrat Shah, Dennis Shen
In particular, we establish entry-wise, i. e., max-norm, finite-sample consistency and asymptotic normality results for matrix completion with MNAR data.
no code implementations • 6 Jul 2021 • Anish Agarwal, Rahul Singh
We propose a procedure for data cleaning, estimation, and inference with data cleaning-adjusted confidence intervals.
no code implementations • NeurIPS 2021 • Anish Agarwal, Abdullah Alomar, Varkey Alumootil, Devavrat Shah, Dennis Shen, Zhi Xu, Cindy Yang
We consider offline reinforcement learning (RL) with heterogeneous agents under severe data scarcity, i. e., we only observe a single historical trajectory for every agent under an unknown, potentially sub-optimal policy.
1 code implementation • 8 Feb 2021 • Hritam Basak, Rohit Kundu, Anish Agarwal, Shreya Giri
In this paper, we propose a deep learning-based approach for the problem, wherein we use a fully convolutional attention network coupled with residual in the residual block (RIR), Residual Channel Attention Block (RCAB), and long and short skip connections.
1 code implementation • 27 Oct 2020 • Anish Agarwal, Devavrat Shah, Dennis Shen
To the best of our knowledge, our prediction guarantees for the fixed design setting have been elusive in both the high-dimensional error-in-variables and synthetic controls literatures.
no code implementations • 24 Jun 2020 • Anish Agarwal, Abdullah Alomar, Devavrat Shah
We introduce and analyze a variant of multivariate singular spectrum analysis (mSSA), a popular time series method to impute and forecast a multivariate time series.
no code implementations • 13 Jun 2020 • Anish Agarwal, Devavrat Shah, Dennis Shen
Towards this, we present a causal framework, synthetic interventions (SI), to infer these $N \times D$ causal parameters while only observing each of the $N$ units under at most two interventions, independent of $D$.
no code implementations • 30 Apr 2020 • Anish Agarwal, Abdullah Alomar, Arnab Sarker, Devavrat Shah, Dennis Shen, Cindy Yang
In essence, the method leverages information from different interventions that have already been enacted across the world and fits it to a policy maker's setting of interest, e. g., to estimate the effect of mobility-restricting interventions on the U. S., we use daily death data from countries that enforced severe mobility restrictions to create a "synthetic low mobility U. S." and predict the counterfactual trajectory of the U. S. if it had indeed applied a similar intervention.
1 code implementation • 31 Mar 2020 • Xiao Lei Zhang, Anish Agarwal
The study of unsupervised learning can be generally divided into two categories: imitation learning and reinforcement learning.
no code implementations • 17 Mar 2019 • Anish Agarwal, Abdullah Alomar, Devavrat Shah
Computationally, tspDB is 59-62x and 94-95x faster compared to LSTM and DeepAR in terms of median ML model training time and prediction query latency, respectively.
no code implementations • NeurIPS 2019 • Anish Agarwal, Devavrat Shah, Dennis Shen, Dogyoon Song
As an important contribution to the Synthetic Control literature, we establish that an (approximate) linear synthetic control exists in the setting of a generalized factor model; traditionally, the existence of a synthetic control needs to be assumed to exist as an axiom.
1 code implementation • 25 Feb 2018 • Anish Agarwal, Muhammad Jehangir Amjad, Devavrat Shah, Dennis Shen
In effect, this generalizes the widely used Singular Spectrum Analysis (SSA) in time series literature, and allows us to establish a rigorous link between time series analysis and matrix estimation.
no code implementations • 25 Apr 2015 • Niangjun Chen, Anish Agarwal, Adam Wierman, Siddharth Barman, Lachlan L. H. Andrew
Making use of predictions is a crucial, but under-explored, area of online algorithms.